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llama2 readme #3315

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4 changes: 3 additions & 1 deletion examples/models/llama2/README.md
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Expand Up @@ -111,9 +111,11 @@ You can export and run the original Llama3 8B model.

2. Export model and generate `.pte` file
```
python -m examples.models.llama2.export_llama --checkpoint <consolidated.00.pth> -p <params.json> -d=fp32 -X -qmode 8da4w -kv --use_sdpa_with_kv_cache --output_name="llama3_kv_sdpa_xnn_qe_4_32.pte" group_size 128 --metadata '{"get_bos_id":128000, "get_eos_id":128001}' --embedding-quantize 4,32
python -m examples.models.llama2.export_llama --checkpoint <consolidated.00.pth> -p <params.json> -kv --use_sdpa_with_kv_cache -X -qmode 8da4w --group_size 128 -d fp32 --metadata '{"get_bos_id":128000, "get_eos_id":128001}' --embedding-quantize 4,32 --output_name="llama3_kv_sdpa_xnn_qe_4_32.pte"
```

Due to the larger vocabulary size of Llama3, we recommend quantizing the embeddings with `--embedding-quantize 4,32` to further reduce the model size.

## (Optional) Finetuning

If you want to finetune your model based on a specific dataset, PyTorch provides [TorchTune](https://github.com/pytorch/torchtune) - a native-Pytorch library for easily authoring, fine-tuning and experimenting with LLMs.
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